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2021

Financial performance and firm efficiency of automotive manufacturers and their suppliers A longitudinal data envelopment analysis

Brandenburg, M. ., & Hahn, G. J. (2021). Financial performance and firm efficiency of automotive manufacturers and their suppliers A longitudinal data envelopment analysis. Logistics Research, 14(1), 26. http://doi.org/10.23773/2021_01

Abstract

A data envelopment analysis (DEA) is presented to assess evolutions of firm efficiency and financial performance in automotive supply chains. A sample of 32 decision-making units (DMUs), 17 globally operating original equipment manufacturers (OEMs) and 15 key suppliers from the automotive industry, is in focus of this analysis in which cost levels and capital requirements are put into relation to sales growth and profit. Cost of goods sold, operating capital, and net fixed assets represent the financial input of a company while sales growth and earnings before interest and taxes (EBIT) reflect the financial output. The financial performance of a firm is indicated by its efficiency, calculated by an input-oriented variable returns to scale model. A multiple linear regression analysis reveals which operational performance factors are predictors of financial performance. A longitudinal DEA approach that covers the years from 2003 to 2017 is chosen to reveal performance evolutions over time. In order to analyze the stability of relationships between efficient firms (peers) and inefficient ones (followers) over time, changes in the performance relationship network are assessed in a graph-theoretic approach. In this study, geographical and structural specifics of DMU groups are taken into account. The study reveals similarities and differences between OEMs and their suppliers regarding the importance of value drivers and detects periods of performance losses and recovery from the global economic crisis.
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2021

Journal Article

Marcus Brandenburg

Gerd Hahn

Automotive industry

Data Envelopment Analysis

Financial performance

Supply networks

2018

A sustainable aggregate production planning model for the chemical process industry

Hahn, G. J., & Brandenburg, M. . (2018). A sustainable aggregate production planning model for the chemical process industry. Computers & Operations Research, 94, 154–168. http://doi.org/10.1016/j.cor.2017.12.011

Abstract

Process industries typically involve complex manufacturing operations and thus require adequate decision support for aggregate production planning (APP). In this paper, we focus on two relevant features of APP in process industry operations: (i) sustainable operations planning involving multiple alternative production modes/routings with specific production-related carbon emission and the social dimension of varying operating rates, (ii) integrated campaign planning with the operational level in order to anticipate production mix/volume/routing decisions on campaign lead times and WIP inventories as well as the impact of variability originating from a stochastic manufacturing environment. We focus on the issue of multi-level chemical production processes and highlight the mutual trade-offs along the triple bottom line concerning economic, environmental and social factors. To this end, production-related carbon emission and overtime working hours are considered as externalized factors as well as internalized ones in terms of resulting costs. A hierarchical decision support tool is presented that combines a deterministic linear programming model and an aggregate stochastic queuing network model. The approach is exemplified at a case example from the chemical industry to illustrate managerial insights and methodological benefits of our approach.
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2018

Journal Article

Gerd Hahn

Marcus Brandenburg

Sustainable production

Aggregate planning

Decision support system

Queuing networks

Chemical process industry
Sustainable aggregate production planning in the chemical process industry - A benchmark problem and dataset

Brandenburg, M. ., & Hahn, G. J. (2018). Sustainable aggregate production planning in the chemical process industry - A benchmark problem and dataset. Data in Brief, 18, 961–967. http://doi.org/10.1016/j.dib.2018.03.064

Abstract

Process industries typically involve complex manufacturing operations and thus require adequate decision support for aggregate production planning (APP). The need for powerful and efficient approaches to solve complex APP problems persists. Problem-specific solution approaches are advantageous compared to standardized approaches that are designed to provide basic decision support for a broad range of planning problems but inadequate to optimize under consideration of specific settings. This in turn calls for methods to compare different approaches regarding their computational performance and solution quality. In this paper, we present a benchmarking problem for APP in the chemical process industry. The presented problem focuses on (i) sustainable operations planning involving multiple alternative production modes/routings with specific production-related carbon emission and the social dimension of varying operating rates and (ii) integrated campaign planning with production mix/volume on the operational level. The mutual trade-offs between economic, environmental and social factors can be considered as externalized factors (production-related carbon emission and overtime working hours) as well as internalized ones (resulting costs). We provide data for all problem parameters in addition to a detailed verbal problem statement. We refer to Hahn and Brandenburg [1] for a first numerical analysis based on and for future research perspectives arising from this benchmarking problem.
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2018

Journal Article

Marcus Brandenburg

Gerd Hahn

Publikationen

2021

Financial performance and firm efficiency of automotive manufacturers and their suppliers A longitudinal data envelopment analysis

Abstract

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2018

A sustainable aggregate production planning model for the chemical process industry

Abstract

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Sustainable aggregate production planning in the chemical process industry - A benchmark problem and dataset

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